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Sequential Recommendation

Sequential recommendation is a sophisticated approach to providing personalized suggestions by analyzing users' historical interactions in a sequential manner. Unlike traditional recommendation systems, which consider items in isolation, sequential recommendation takes into account the temporal order of user actions. This method is particularly valuable in domains where the sequence of events matters, such as streaming services, e-commerce platforms, and social media.

Papers

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Showing 271–280 of 554 papers

TitleStatusHype
Pay Attention to Attention for Sequential Recommendation—0
Generative Diffusion Models for Sequential Recommendations—0
Generate and Instantiate What You Prefer: Text-Guided Diffusion for Sequential Recommendation—0
Cross-Domain Sequential Recommendation via Neural Process—0
Intent-Enhanced Data Augmentation for Sequential Recommendation—0
Direct Preference Optimization for LLM-Enhanced Recommendation Systems—0
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services—0
Multimodal Point-of-Interest Recommendation—0
TTT4Rec: A Test-Time Training Approach for Rapid Adaption in Sequential RecommendationCode0
Autoregressive Generation Strategies for Top-K Sequential Recommendations—0
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